Enterprise content management systems (ECMS) have been introduced in the regular workflow in the analytical, chemical and pharmaceutical industries which usually face a large amount of analytical data to be processed, properly assigned, structured and evaluated/analyzed. One of the goals of data processing and organization is to arrive at an arrangement of the data useful for applying multivariate methods of data analysis (MVA). This type of analysis is based on the statistical principle of multivariate statistics which involves observation and analysis of more than one statistical variable at a time.
Up to the present date, it has proven difficult to implement an automatic procedure for providing suitable arrangements of data for multivariate analysis. This is so because the results obtained in an analytical experiment, e.g. the relative retention times of peaks measured in an HPLC experiment (HPLC=High Pressure Liquid Chromatography), vary when the working parameters and conditions used in the experiment such as temperature, pressure, pH of solvents, e.g. the composition and pH of the mobile phase used in HPLC, are changed. Accordingly, the user of the analytical method has to manually organize the measured data and associate it with the attribute of interest of the sample analyzed. For example, in a measured assay of HPLC peaks, i.e. a chromatogram, which is an example of a specific set of data as discussed above, each HPLC peak has to be linked to a specific impurity, which is the attribute of interest in this specific case. This has to be done manually for each chromatogram measured which presents a significant workload. Other attributes of interest may be a specific colour, smell, particle size or particle size distribution, etc. The thus processed data could then be used for further analysis and optimization procedures.
It is an object of the present invention to provide a process which allows for the above type of analysis in a more efficient manner.